Interpretation of multivariate metabolomic models through network-guided perturbation-based explanations.

Area of Science:

• * Computational Biology
• * Bioinformatics
• * Systems Biology

Background:

• * Multivariate modeling is essential for analyzing complex patterns in metabolomic data.
• * Interpreting these complex models remains a significant challenge in the field.

Purpose of the Study:

• * To develop a novel network-guided framework to enhance the interpretability of multivariate models in metabolomics.
• * To improve biochemical explanations by grouping metabolites based on network communities.

Main Methods:

• * A network-guided framework was developed to group metabolites into communities identified within metabolic networks.
• * The framework utilizes KEGG metabolites and enzyme-catalyzed reactions for network construction.
• * The approach was applied to postprandial plasma metabolomic data.

Main Results:

• * Grouping metabolites into network communities enhances perturbation-based analysis of multivariate models.
• * This method provides complementary biochemical interpretation that extends beyond fixed pathways.
• * The results demonstrate the utility of network structure for data interpretation.

Conclusions:

• * The proposed network-guided strategy offers a novel tool for improving model interpretability in complex biological datasets.
• * It facilitates hypothesis generation by providing deeper biochemical insights.
• * The approach is model-agnostic and transferable across different omics domains and multivariate methods.